Excellent question! **Semantic search** goes beyond keyword matching to understand the *meaning* and *context* of queries. Here's how to enable semantic search across each AWS database service:
## **Two Main Approaches for Semantic Search**
1. **Vector Search**: Converting text/images into embeddings (vectors) and searching by similarity
2. **LLM-Enhanced Search**: Using Large Language Models to understand query intent
---
## **How to Enable Semantic Search on Each Service**
### **1. Amazon OpenSearch Service / OpenSearch Serverless**
**Native Support:** ✅ **Best suited for semantic search**
- **OpenSearch Neural Search Plugin** (built-in):
- Supports vector search using ML models (BERT, sentence-transformers)
- Can generate embeddings or ingest pre-computed embeddings
- `k-NN` (k-nearest neighbors) index for similarity search
- **Implementation:**
```json
// 1. Create index with vector field
{
"settings": {"index.knn": true},
"mappings": {
"properties": {
"embedding": {
"type": "knn_vector",
"dimension": 768
},
"text": {"type": "text"}
}
}
}
// 2. Query using semantic similarity
{
"query": {
"knn": {
"embedding": {
"vector": [0.1, 0.2, ...], // Query embedding
"k": 10
}
}
}
}
```
- **Use Cases:** Hybrid search (combining keyword + semantic), RAG (Retrieval-Augmented Generation)
### **2. Amazon Aurora PostgreSQL**
**Native Support:** ✅ **via pgvector extension**
- **pgvector extension** adds vector similarity search capabilities:
```sql
-- Enable extension
CREATE EXTENSION vector;
-- Create table with vector column
CREATE TABLE documents (
id SERIAL PRIMARY KEY,
content TEXT,
embedding vector(1536) -- OpenAI embeddings dimension
);
-- Create index for fast similarity search
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops);
-- Semantic search query
SELECT content, embedding <=> '[0.1, 0.2, ...]' as similarity
FROM documents
ORDER BY similarity
LIMIT 10;
```
- **Integration:** Use AWS Lambda + Amazon Bedrock/SageMaker to generate embeddings
- **Best for:** Applications already on PostgreSQL needing semantic capabilities
### **3. Amazon RDS for PostgreSQL**
**Native Support:** ✅ **Same as Aurora PostgreSQL**
- Also supports `pgvector` extension (PostgreSQL 11+)
- Similar implementation to Aurora
- **Limitation:** May have slightly lower performance than Aurora for large-scale vector operations
### **4. Amazon DynamoDB**
**No Native Support:** ❌ But can be enabled via:
- **DynamoDB + OpenSearch/Elasticache** pattern:
1. Store metadata in DynamoDB
2. Store vectors in OpenSearch/Amazon MemoryDB (Redis with RedisVL)
3. Use DynamoDB Streams to keep them in sync
- **DynamoDB + S3** pattern:
1. Store vectors as Parquet files in S3
2. Use Athena/Pandas for similarity search
3. Store metadata in DynamoDB
- **Bedrock Knowledge Bases** (newest approach):
- AWS-managed RAG solution
- Automatically chunks documents, generates embeddings, stores in vector database
- Can use OpenSearch as vector store with DynamoDB as metadata store
### **5. Amazon DocumentDB**
**Limited Native Support:** ⚠️ **Workaround needed**
- No native vector data type
- **Solutions:**
1. **Store embeddings as arrays**: `"embedding": [0.1, 0.2, ...]`
2. **Use cosine similarity in application code** (not efficient at scale)
3. **Hybrid approach**: Store in DocumentDB, index vectors in OpenSearch
- Not recommended for production semantic search at scale
### **6. Amazon MemoryDB for Redis**
**Native Support:** ✅ **via RedisVL (Redis Vector Library)**
- **Redis Stack** includes RediSearch with vector search:
```bash
# Create index with vector field
FT.CREATE doc_idx
ON HASH
PREFIX 1 doc:
SCHEMA
content TEXT
embedding VECTOR
FLAT 6
TYPE FLOAT32
DIM 768
DISTANCE_METRIC COSINE
# Search similar vectors
FT.SEARCH doc_idx
"(*)=>[KNN 10 @embedding $query_vector]"
PARAMS 2 query_vector "<binary_vector>"
DIALECT 2
```
- **Advantage:** Ultra-low latency (millisecond search)
- **Best for:** Real-time semantic search, caching vector results
### **7. Amazon Neptune**
**Limited Support:** ⚠️ **Graph-enhanced semantic search**
- Not designed for vector similarity search
- **Alternative approach:** Graph-augmented semantic search
1. Use OpenSearch for vector search
2. Use Neptune to navigate relationships in results
3. Example: Find semantically similar documents, then find related entities in graph
- **Use Case:** Knowledge graphs with semantic understanding
---
## **AWS-Managed Semantic Search Solutions**
### **Option 1: Amazon Bedrock Knowledge Bases**
**Fully managed RAG solution:**
1. Upload documents to S3
2. Bedrock automatically:
- Chunks documents
- Generates embeddings (using Titan or Cohere)
- Stores vectors in supported vector database
3. Query via Retrieve API
4. **Supported vector stores:** Aurora PostgreSQL, OpenSearch, Pinecone
### **Option 2: Amazon Kendra**
**Enterprise semantic search service:**
- Pre-trained models for understanding natural language
- Connectors for various data sources
- No need to manage embeddings/models
- More expensive but requires zero ML expertise
### **Option 3: Custom Architecture with SageMaker**
```
Data Sources → SageMaker (Embedding Model) → Vector Store → Query Service
↓ ↓ ↓ ↓
DynamoDB Lambda Functions OpenSearch API Gateway
(Metadata) (Orchestration) (Vectors) (Client)
```
---
## **Recommendations by Use Case**
| Use Case | Recommended AWS Stack |
|----------|----------------------|
| **Starting fresh** | **OpenSearch Service** (neural plugin) or **Bedrock Knowledge Bases** |
| **Already on PostgreSQL** | **Aurora PostgreSQL** with pgvector |
| **Real-time/low-latency** | **MemoryDB for Redis** (RedisVL) |
| **Enterprise/zero-ML** | **Amazon Kendra** |
| **Serverless RAG** | **Bedrock Knowledge Bases** + **DynamoDB** |
| **High-scale hybrid search** | **OpenSearch** (combines BM25 + vector search) |
## **Quick Start Path**
For most applications starting semantic search on AWS:
1. **For simplicity**: Use **Amazon Bedrock Knowledge Bases**
2. **For control/flexibility**: Use **OpenSearch Service with neural plugin**
3. **For existing PostgreSQL apps**: Add **pgvector to Aurora PostgreSQL**
4. **For real-time needs**: Use **MemoryDB for Redis with RedisVL**
The key is generating quality embeddings. AWS offers:
- **Amazon Titan Embeddings** (via Bedrock)
- **SageMaker JumpStart** (pre-trained models)
- **SageMaker Training** (custom models)
